This repository contains GGUF quants for meta-models/Muse-Glimmer-30B, created using llama.cpp. Muse Glimmer is a 30-billion-parameter multimodal model distilled from Muse Spark, purpose-built for autonomous agentic workflows, long-horizon multi-step reasoning, SWE-bench coding tasks, and reliable function calling on consumer hardware.

File Availability & Recommended Hardware

To run vision inputs, download one core model file (.gguf) along with one multimodal vision projector (mmproj-*.gguf).

Core Model Files

File Name	Size	Quantization	Rec. Memory / VRAM	Description
`Muse-Glimmer-30B-Q8_0.gguf`	29.6 GB	Q8_0	32 GB - 48 GB	Maximum precision. Virtually lossless retention compared to BF16.
`Muse-Glimmer-30B-Q6_K.gguf`	22.9 GB	Q6_K	28 GB - 32 GB	Near-lossless output precision. Great for 32GB system/VRAM setup.
`Muse-Glimmer-30B-Q5_K_M.gguf`	19.8 GB	Q5_K_M	24 GB	High Quality balance. Ideal fit for GPUs with 24GB VRAM (e.g., RTX 3090/4090/5090).
`Muse-Glimmer-30B-Q4_K_M.gguf`	16.9 GB	Q4_K_M	20 GB - 24 GB	Recommended Sweet Spot. Optimal trade-off between speed, memory, and reasoning capacity.
`Muse-Glimmer-30B-IQ4_NL.gguf`	16.1 GB	IQ4_NL	20 GB	Non-Linear 4-bit importance matrix quantization. Strong performance under 17GB.
`Muse-Glimmer-30B-IQ4_XS.gguf`	15.3 GB	IQ4_XS	18 GB - 20 GB	Extra-small 4-bit iQuant for constrained VRAM environments.
`Muse-Glimmer-30B-Q3_K_M.gguf`	14.0 GB	Q3_K_M	16 GB - 18 GB	Standard 3-bit K-quant. Good option for 16GB VRAM cards.
`Muse-Glimmer-30B-IQ3_M.gguf`	13.1 GB	IQ3_M	16 GB	3-bit medium importance quant with better reasoning recovery than baseline Q3.
`Muse-Glimmer-30B-IQ3_XS.gguf`	12.3 GB	IQ3_XS	14 GB - 16 GB	3-bit extra-small iQuant for lower memory targets.
`Muse-Glimmer-30B-IQ3_XXS.gguf`	11.5 GB	IQ3_XXS	12 GB - 16 GB	Highly compressed 3-bit iQuant. Fits tight memory budgets.

Vision Projector Files (`mmproj`)

File Name	Size	Precision	Usage
`mmproj-Muse-Glimmer-30B-BF16.gguf`	3.85 GB	BF16	Full-precision ~1.8B ViT perception projector for maximum image fidelity.
`mmproj-Muse-Glimmer-30B-Q8_0.gguf`	2.05 GB	Q8_0	Recommended. 8-bit quantized vision projector preserving high image understanding at nearly half the RAM.

Quickstart & Usage

1. `llama.cpp` CLI (With Vision Support)

To run the model with multimodal vision capability:

bash

# Start server with vision support
llama-server \
  -m Muse-Glimmer-30B-Q4_K_M.gguf \
  --mmproj mmproj-Muse-Glimmer-30B-Q8_0.gguf \
  -c 131072 \
  --port 8080

Abiray/Muse-Glimmer-30B-GGUF

Muse-Glimmer-30B - GGUF Quants

File Availability & Recommended Hardware

Core Model Files

Vision Projector Files (mmproj)

Quickstart & Usage

1. llama.cpp CLI (With Vision Support)

Abiray/
Muse-Glimmer-30B-GGUF

Vision Projector Files (`mmproj`)

1. `llama.cpp` CLI (With Vision Support)